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release: cut v1.0.0

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Prepare the first public 1.0.0 release and finish the remaining CI hardening work.

Highlights:
- align Python, Rust, WASM, Conda, API, MCP, and docs version metadata to 1.0.0
- promote package metadata to Production/Stable and update stability/versioning docs for the stable series
- move the accumulated Unreleased notes into a dated 1.0.0 changelog section and keep a fresh top-level Unreleased block
- strengthen the changelog checker so it validates a single top-level Unreleased section
- fix the CI/package support mismatch by declaring Python >=3.10 consistently and gating pandas-ta extras to Python 3.12+
- restore Sphinx autodoc compatibility for documented ferro_ta.<module> imports by registering module aliases
- make the TA-Lib benchmark guardrail less flaky by checking median and tail-percentile speedups instead of failing on a single mild outlier
- switch PyPI publishing to OIDC-only trusted publishing and wire the changelog check into the required CI gate
- apply the Ruff-driven cleanup across the Python and test tree and refresh uv/cargo lockfiles

Validated locally:
- python3 scripts/check_changelog.py
- uv run --with ruff ruff check python tests
- uv run --with ruff ruff format --check python tests
- uv lock --check
- sphinx-build -b html docs docs/_build -W --keep-going
- build/install the ferro_ta 1.0.0 wheel successfully
This commit is contained in:
Pratik Bhadane
2026-03-23 23:57:30 +05:30
parent 7a5a220dfe
commit 307beeca02
47 changed files with 1822 additions and 573 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/integration/test_streaming_accuracy.py
+66 -56
View File
@@ -197,9 +197,9 @@ class TestStreamingATR:
# Streaming
streamer = StreamingATR(period=period)
stream_out = np.array([
streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)
])
stream_out = np.array(
[streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
)
# Compare only the overlap region where both arrays are valid
mask = np.isfinite(batch_out) & np.isfinite(stream_out)
@@ -207,9 +207,9 @@ class TestStreamingATR:
"""ATR values should be non-negative."""
period = 14
streamer = StreamingATR(period=period)
stream_out = np.array([
streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)
])
stream_out = np.array(
[streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
)
# Filter out NaN values
valid = stream_out[~np.isnan(stream_out)]
@@ -222,15 +222,21 @@ class TestStreamingATR:
streamer = StreamingATR(period=period)
# First pass
first_pass = np.array([
streamer.update(h, l, c) for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
])
first_pass = np.array(
[
streamer.update(h, l, c)
for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
]
)
# Reset and second pass
streamer.reset()
second_pass = np.array([
streamer.update(h, l, c) for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
])
second_pass = np.array(
[
streamer.update(h, l, c)
for h, l, c in zip(HIGH[:50], LOW[:50], CLOSE[:50])
]
)
assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-12)
@@ -253,7 +259,9 @@ class TestStreamingBBands:
verify proximity with atol=0.2 and confirm internal consistency separately.
"""
# Batch
batch_upper, batch_middle, batch_lower = ferro_ta.BBANDS(CLOSE, timeperiod=period)
batch_upper, batch_middle, batch_lower = ferro_ta.BBANDS(
CLOSE, timeperiod=period
)
# Streaming
streamer = StreamingBBands(period=period, nbdevup=2.0, nbdevdn=2.0)
@@ -265,8 +273,9 @@ class TestStreamingBBands:
# Compare only overlapping valid region
mask = np.isfinite(batch_middle)
# Middle band (SMA) must match exactly
assert np.allclose(stream_middle[mask], batch_middle[mask], atol=1e-10), \
assert np.allclose(stream_middle[mask], batch_middle[mask], atol=1e-10), (
"BBands middle (SMA) must match batch exactly"
)
# Upper/lower: streaming uses sample std; batch uses population std — use atol=0.2
assert np.allclose(stream_upper[mask], batch_upper[mask], atol=0.2)
assert np.allclose(stream_lower[mask], batch_lower[mask], atol=0.2)
@@ -285,7 +294,9 @@ class TestStreamingBBands:
# Compare all three bands
for i in range(len(first_pass)):
assert np.allclose(first_pass[i], second_pass[i], equal_nan=True, atol=1e-14)
assert np.allclose(
first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
)
# ---------------------------------------------------------------------------
@@ -341,7 +352,9 @@ class TestStreamingMACD:
# Compare all three outputs
for i in range(len(first_pass)):
assert np.allclose(first_pass[i], second_pass[i], equal_nan=True, atol=1e-14)
assert np.allclose(
first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
)
# ---------------------------------------------------------------------------
@@ -356,19 +369,12 @@ class TestStreamingStoch:
"""Streaming Stochastic should match batch Stochastic."""
# Batch
batch_slowk, batch_slowd = ferro_ta.STOCH(
HIGH, LOW, CLOSE,
fastk_period=5, slowk_period=3,
slowd_period=3
HIGH, LOW, CLOSE, fastk_period=5, slowk_period=3, slowd_period=3
)
# Streaming
streamer = StreamingStoch(
fastk_period=5, slowk_period=3,
slowd_period=3
)
stream_results = [
streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)
]
streamer = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
stream_results = [streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
stream_slowk = np.array([r[0] for r in stream_results])
stream_slowd = np.array([r[1] for r in stream_results])
@@ -380,13 +386,8 @@ class TestStreamingStoch:
def test_stoch_range_zero_to_hundred(self):
"""Stochastic values should be in range [0, 100]."""
streamer = StreamingStoch(
fastk_period=5, slowk_period=3,
slowd_period=3
)
stream_results = [
streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)
]
streamer = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
stream_results = [streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
stream_slowk = np.array([r[0] for r in stream_results])
stream_slowd = np.array([r[1] for r in stream_results])
@@ -401,10 +402,7 @@ class TestStreamingStoch:
def test_reset_gives_same_result(self):
"""Reset and re-feed should give identical output."""
streamer = StreamingStoch(
fastk_period=5, slowk_period=3,
slowd_period=3
)
streamer = StreamingStoch(fastk_period=5, slowk_period=3, slowd_period=3)
# First pass
first_pass = [
@@ -419,7 +417,9 @@ class TestStreamingStoch:
# Compare
for i in range(len(first_pass)):
assert np.allclose(first_pass[i], second_pass[i], equal_nan=True, atol=1e-14)
assert np.allclose(
first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
)
# ---------------------------------------------------------------------------
@@ -437,9 +437,12 @@ class TestStreamingVWAP:
# Streaming (cumulative)
streamer = StreamingVWAP()
stream_out = np.array([
streamer.update(h, l, c, v) for h, l, c, v in zip(HIGH, LOW, CLOSE, VOLUME)
])
stream_out = np.array(
[
streamer.update(h, l, c, v)
for h, l, c, v in zip(HIGH, LOW, CLOSE, VOLUME)
]
)
# Compare
assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-10)
@@ -451,9 +454,12 @@ class TestStreamingVWAP:
# Streaming (cumulative)
streamer = StreamingVWAP()
stream_out = np.array([
streamer.update(h, l, c, v) for h, l, c, v in zip(HIGH, LOW, CLOSE, VOLUME)
])
stream_out = np.array(
[
streamer.update(h, l, c, v)
for h, l, c, v in zip(HIGH, LOW, CLOSE, VOLUME)
]
)
# Compare
assert np.allclose(stream_out, batch_out, equal_nan=True, atol=1e-10)
@@ -463,17 +469,21 @@ class TestStreamingVWAP:
streamer = StreamingVWAP()
# First pass
first_pass = np.array([
streamer.update(h, l, c, v)
for h, l, c, v in zip(HIGH[:50], LOW[:50], CLOSE[:50], VOLUME[:50])
])
first_pass = np.array(
[
streamer.update(h, l, c, v)
for h, l, c, v in zip(HIGH[:50], LOW[:50], CLOSE[:50], VOLUME[:50])
]
)
# Reset and second pass
streamer.reset()
second_pass = np.array([
streamer.update(h, l, c, v)
for h, l, c, v in zip(HIGH[:50], LOW[:50], CLOSE[:50], VOLUME[:50])
])
second_pass = np.array(
[
streamer.update(h, l, c, v)
for h, l, c, v in zip(HIGH[:50], LOW[:50], CLOSE[:50], VOLUME[:50])
]
)
assert np.allclose(first_pass, second_pass, equal_nan=True, atol=1e-14)
@@ -498,9 +508,7 @@ class TestStreamingSupertrend:
# Streaming
streamer = StreamingSupertrend(period=period, multiplier=multiplier)
stream_results = [
streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)
]
stream_results = [streamer.update(h, l, c) for h, l, c in zip(HIGH, LOW, CLOSE)]
stream_line = np.array([r[0] for r in stream_results])
stream_dir = np.array([r[1] for r in stream_results])
@@ -527,4 +535,6 @@ class TestStreamingSupertrend:
# Compare
for i in range(len(first_pass)):
assert np.allclose(first_pass[i], second_pass[i], equal_nan=True, atol=1e-14)
assert np.allclose(
first_pass[i], second_pass[i], equal_nan=True, atol=1e-14
)